# Introduction

This is a weakly supervised ASR recipe for the LibriSpeech (clean 100 hours) dataset. We training a
conformer model using BTC/OTC with transcripts with synthetic errors. In this README, we will describe
the task and the BTC/OTC training process.

## Task
We propose BTC/OTC to directly train an ASR system leveraging weak supervision, i.e., speech with non-verbatim transcripts.


<div style="display: flex;flex; justify-content: space-between">
  <figure style="flex: 2; text-align: center; margin: 5px;">
    <img src="figures/sub.png" alt="Image 1" width="30%" />

  </figure>
  <figure style="flex: 2; text-align: center; margin: 5px;">
    <img src="figures/ins.png" alt="Image 2" width="30%" />

  </figure>
  <figure style="flex: 2; text-align: center;margin: 5px;">
    <img src="figures/del.png" alt="Image 3" width="30%" />

  </figure>
</div>
<figcaption> Examples of error in the transcript. The grey box is the verbatim transcript and the red box is the inaccurate transcript. Inaccurate words are marked in bold.</figcaption> <br>

This is achieved by using a special token $\star$ to model uncertainties (i.e., substitution errors, insertion errors, and deletion errors) 
within the WFST framework during training.\
we modify $G(\mathbf{y})$ by adding self-loop arcs into each state and bypass arcs into each arc. 
  <p align="center">
    <img src="figures/otc_g.png" alt="Image Alt Text" width="50%" />

  </p>

After composing the modified WFST $G_{\text{otc}}(\mathbf{y})$ with $L$ and $T$, the OTC training graph is shown in this figure:
<figure style="text-align: center">
  <img src="figures/otc_training_graph.drawio.png" alt="Image Alt Text" />
  <figcaption>OTC training graph. The self-loop arcs and bypass arcs are highlighted in green and blue, respectively.</figcaption>
</figure>

The $\star$ is represented as the average probability of all non-blank tokens.
  <p align="center">
    <img src="figures/otc_emission.drawio.png" width="50%" />
  </p>

The weight of $\star$ is the log average probability of "a" and "b": $\log \frac{e^{-1.2} + e^{-2.3}}{2} = -1.6$ and $\log \frac{e^{-1.9} + e^{-0.5}}{2} = -1.0$ for 2 frames.

## Description of the recipe